The PSC3 can feasibility studies to assess whether it makes sense to run a certain algorithm/program on an accelerator. The interpretation of 'make sense' is case-dependent, it can be expressed in terms of runtime requirements, performance expectations or possible speed-ups. Given one or a few platforms, a problem, an algorithm and, preferably, a CPU-implementation, the PSC3 can estimate and study the potential acceleration on GPUs.

This includes:

Identification of the computational bottlenecks: the parts of the cpu-implementation consuming most of the computational performance.

Analysis for the selected parts of:

the peak performance: whether bound by computational requirements or bound by memory transfer

the bottlenecks: the causes of performance degradation with respect to the peak performance. Our expertise enables us to quantify this performance degradation en estimate the efficiency.

the possible solutions/optimizations that might partially overcome the bottlenecks

development of proof-of-concept implementations, each testing a certain solution or performance aspect. These implementations might not be full-fledged gpu implementations of the desired algorithm, they merely mimic the computational requirements of the algorithm. They are made to test and estimate the performance.

Study of the match of the algorithm with a selected set of hardware platforms. E.g. to study AMD versus NVIDIA or 2nd versus 3rd generation GPUs.

Setting up a global report in which the overall performance is provided in terms of the major domain performance metric, such as runtime or more case-dependent metrics such as pixels per second or frames per second. The performance will be estimated based on typical data inputs.

Through the entire study, progress will be communicated by intermediate minutes so that we can decide together which paths to explore further.

The cost for the study will be based on the actual number of working days and the daily fee.